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000268890 005__ 20240923164113.0
000268890 037__ $$aDZNE-2024-00389
000268890 1001_ $$0P:(DE-HGF)0$$aCampbell, Alexander$$b0
000268890 1112_ $$aMedical Imaging with Deep Learning$$cNashville, Tenn.$$d2023-07-10 - 2023-07-12$$gMIDL 2023$$wUSA
000268890 245__ $$aDBGDGM: Dynamic Brain Graph Deep Generative Model
000268890 260__ $$c2024
000268890 300__ $$a1346 - 1371
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000268890 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1727078219_3424
000268890 4900_ $$v227
000268890 500__ $$aISSN 2640-3498: Proceedings of Machine Learning Research
000268890 520__ $$aGraphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings. Specifically, DBGDGM represents brain graph nodes as embeddings sampled from a distribution over communities that evolve over time. We parameterise this community distribution using neural networks that learn from subject and node embeddings as well as past community assignments. Experiments demonstrate DBGDGM outperforms baselines in graph generation, dynamic link prediction, and is comparable for graph classification. Finally, an analysis of the learnt community distributions reveals overlap with known FCNs reported in neuroscience literature.
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000268890 7001_ $$0P:(DE-2719)9002679$$aSpasov, Simeon$$b1$$udzne
000268890 7001_ $$0P:(DE-HGF)0$$aToschi, Nicola$$b2
000268890 7001_ $$0P:(DE-HGF)0$$aLio, Pietro$$b3
000268890 773__ $$p1346-1371$$v227$$y2024
000268890 8564_ $$uhttps://proceedings.mlr.press/v227/campbell24b.html
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000268890 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)9002679$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b1$$kDZNE
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000268890 9201_ $$0I:(DE-2719)1013030$$kAG Mukherjee$$lStatistics and Machine Learning$$x0
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